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From decoding microbial ecosystems to analyzing vast genetic datasets, modern biology increasingly relies on high-powered software tools that can process massive, complex streams of information. A team led by Arizona State University (ASU), University of California San Diego and several other institutions recently published an article in Nature Methods to showcase one of those essential tools — scikit-bio, an open-source Python library that has become a global mainstay in biological data analysis, allowing analyses that once demanded months of time to be completed within hours.

“The scikit-bio tool allows me to efficiently study microbial ecosystems at scale,” said Celeste Allaband, a postdoctoral scholar at UC San Diego who investigates digestive microbiomes. “I am able to better understand, analyze and communicate important data from massive systems such as those from the Human Microbiome Project and the American Gut Project.”

Representing a collaboration among an international community of contributors, scikit-bio is led by Qiyun Zhu, an ASU assistant professor who specializes in bioinformatics and microbiome research. Zhu said that the platform is designed to help researchers navigate the explosion of “omics” data from genetic sequences to microbial profiles —  enabling studies that span entire ecosystems rather than single experiments.

Among the project’s contributors is Igor Sfiligoi, a research scientist at the San Diego Supercomputer Center (SDSC), a pillar of the School of Computing, Information and Data Sciences (SCIDS), who has helped bridge the gap between advanced computing and data-intensive biology.

“We see scikit-bio as an essential infrastructure for biological data science,” Sfiligoi said. “As the scale of biological data continues to grow, resources like this are what will enable research to keep pace.”

Although the software has quietly supported researchers for more than a decade, the Nature Methods publication marks a milestone — formally recognizing scikit-bio as a mature, community-driven platform underpinning research across multiple fields of biology. The paper also reflects a growing emphasis on open, shared software as vital scientific infrastructure in an era where biological data is expanding at unprecedented rates.